TL;DR
It is demonstrated that FHE-enabled models remain susceptible to both evasion and backdoor attacks, underscoring the need for stronger defenses in encrypted ML systems.
Knowledge distillation (KD) is increasingly used in federated learning (FL) because it enables clients to exchange predictions, features, prototypes, or synthetic knowledge rather than full model parameters. This change can reduce communication and support heterogeneous models, but it also changes the privacy and secur...
Experimental results indicate that TMI-VFL achieves an effective trade-off between privacy protection and model utility, providing a practical solution for secure VFL.
To mitigate the attacks of transferable adversarial examples, a defense mechanism stemming from the transferability of model robustness by adversarial training is designed, gaining insights into adversarial examples and the vulnerability of federated learning systems.
Federated learning (FL) enables collaborative model training without sharing raw data, but remains vulnerable to Byzantine attacks and privacy leakage. Existing privacy-preserving robust FL schemes suffer from prohibitive computation and communication overheads, particularly on resourceconstrained clients. To address t...
BackDFL is presented, a unified benchmark for systematically evaluating DFL under realistic and adaptive backdoor attacks, and demonstrates that both state-of-the-art Byzantine-robust DFL methods and adapted FL backdoor defenses fail under modest malicious participation rates, especially in heterogeneous settings.
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